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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/kclary/variability-RL

공식 코드

Let's Play Again: Variability of Deep Reinforcement Learning Agents in Atari Environments

https://github.com/kengz/SLM-Lab

공식 코드

SLM Lab: A Comprehensive Benchmark and Modular Software Framework for Reproducible Deep Reinforcement Learning

https://github.com/kenjyoung/MinAtar

공식 코드

MinAtar: An Atari-Inspired Testbed for Thorough and Reproducible Reinforcement Learning Experiments

https://github.com/keqinw/man

공식 코드

MAN: Multi-Action Networks Learning

https://github.com/khurramjaved96/atari-prediction-benchmark

공식 코드

Scalable Real-Time Recurrent Learning Using Columnar-Constructive Networks

https://github.com/kkane99/MANGER_code

공식 코드

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

https://github.com/kkane99/manger

공식 코드

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

https://github.com/kzl/decision-transformer

공식 코드

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/leor-c/m3

공식 코드

Uncovering Untapped Potential in Sample-Efficient World Model Agents

https://github.com/leor-c/simulus

공식 코드

Uncovering Untapped Potential in Sample-Efficient World Model Agents

https://github.com/lili-chen/SEER

공식 코드

Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

https://github.com/liuruoze/Raw-vs-Human-in-AlphaStar

공식 코드

Rethinking of AlphaStar

https://github.com/liuruoze/hiernet-sc2

공식 코드

On Efficient Reinforcement Learning for Full-length Game of StarCraft II

https://github.com/liuruoze/mini-AlphaStar

공식 코드

An Introduction of mini-AlphaStar

https://github.com/liuruoze/thought-sc2-model-experiment

공식 코드

Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning

https://github.com/liuxhym/adapmen

공식 코드

How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement

https://github.com/lns/dapo

공식 코드

Divergence-Augmented Policy Optimization

https://github.com/luchang1113/hep-llm-play-starcraftii

공식 코드

Hierarchical Expert Prompt for Large-Language-Model: An Approach Defeat Elite AI in TextStarCraft II for the First Time

https://github.com/lucidrains/perceiver-pytorch

공식 코드

Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models

https://github.com/luciferkonn/dt_mem

공식 코드

Think Before You Act: Decision Transformers with Working Memory

https://github.com/lxxxxr/ices

공식 코드

Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning

https://github.com/lxxxxr/kaleidoscope

공식 코드

Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning

https://github.com/mafi412/evolution-strategies-and-decision-transformers

공식 코드

Utilizing Evolution Strategies to Train Transformers in Reinforcement Learning

https://github.com/maitchison/PPO

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DNA: Proximal Policy Optimization with a Dual Network Architecture

https://github.com/maitchison/atari-5

공식 코드

Atari-5: Distilling the Arcade Learning Environment down to Five Games

https://github.com/manueleberhardinger/ec-rl

공식 코드

Learning of Generalizable and Interpretable Knowledge in Grid-Based Reinforcement Learning Environments

https://github.com/marlbenchmark/on-policy

공식 코드

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

https://github.com/marmotlab/fcmnet

공식 코드

FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems

https://github.com/matrl-project/matrl

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A Game-Theoretic Approach to Multi-Agent Trust Region Optimization

https://github.com/mchldann/SpectralDQN

공식 코드

Adapting to Reward Progressivity via Spectral Reinforcement Learning

https://github.com/mcmachado/b-pro

공식 코드

State of the Art Control of Atari Games Using Shallow Reinforcement Learning

https://github.com/mcmachado/count_based_exploration_sr

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Count-Based Exploration with the Successor Representation

https://github.com/mcmachado/options

공식 코드

A Laplacian Framework for Option Discovery in Reinforcement Learning

https://github.com/mengli11235/mst_dim

공식 코드

Unsupervised Representation Learning in Partially Observable Atari Games

https://github.com/mengli11235/swin-dqn

공식 코드

Deep Reinforcement Learning with Swin Transformers

https://github.com/mgoulao/tov-vicreg

공식 코드

Pretraining the Vision Transformer using self-supervised methods for vision based Deep Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.